In order to obtain a representative sample and to compare the situation of collaboration consumption in the countries of the European Union, the European Commission [45] dedicated a Flash Eurobarometer (number 438) to a survey of the use of collaborative economy platforms. Flash Eurobarometers are ad hoc statistical operations consisting of short—landline and mobile—telephone interviews on a topic of interest. Flash Barometer 438 obtained data on the use of collaborative economy platforms from a sample of 14,050 citizens aged 15 years and above in the 28 countries of the European Union (Belgium, Bulgaria, Czech Re public, De nmark, Ger many, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, the Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, and the United Kingdom) through approximately 500 interviews per country. The universe of the survey consisted of the 412,630,644 European Union citizens aged 15 years and above. The sample design for each country was probabilistic and representative. The margins of error at the 95% confidence level in the case of maximum indetermination (p = q = 50) were +0.4% for the entire sample, and around +1.9% for individual country samples. The fieldwork was carried out on March 15 and 16, 2016.
The questionnaire defines a collaborative platform (CP) as “an Internet-based tool that enables transactions between people providing and using a service. They can be used for a wide range of services, from renting accommodation and car shar- ing to small household jobs ([45], p. 29).” Based on that approach, the survey asked the respondents about their awareness of such platforms and gave them the follow- ing options for their answers on use: (1) unaware (UNAWARE) or “You have never heard of these platforms”; (2) aware but does not use (AWNOTUSE) or “You have heard of these platforms but you have never visited them”; (3) initial use (INIUSE) or “You have been on one or more of these platforms and paid for a service once”;
(4) occasional use (OCCAUSE) or “You use the services of these platforms occa- sionally (once every few months)”; and (5) regular use (REGUSE) or “You use the services of these platforms regularly (at least every month).” For all users of such platforms (TOTUSE), which includes initial use, occasional use, and regular use, the survey also gathered data about providing goods and services and gave the respondents the following options for their answers: (1) no provision (NOPROV) or “No, you haven’t”; (2) initial provision (INIPROV) or “You have offered a service on one or more of these platforms once”; (3) occasional provision (OCCAPROV) or “You offer services via these platforms occasionally (once every few months)”;
and (4) regular provision (REGPROV) or “You offer services via these platforms regularly (every month).” All providers of such platforms (TOTPROV) include initial provision, occasional provision, and regular provision. The various options
of those two variables were transformed into individual variables. All of these new individual variables were dichotomous, where 1 = the respondent was aware of and used or provided goods or services via collaborative platforms, and 0 = the respon- dent answered otherwise.
Having stipulated the levels of use and provision, the survey looked at the driv- ing factors (benefits) and impeding factors (problems) of collaborative platforms compared to the traditional forms of commerce of goods and services. Regarding the driving factors, the survey gave those respondents who were aware of and users of collaborative platforms the following options for their answers: (1) service cost (PRICE) or “It is cheaper or free”; (2) service newness (NEWNESS) or “It offers new or different services”; (3) service convenience (CONVEN) or “The access to services is organized in a more convenient way”; and (4) nonmonetary exchanges (NONMONET) or “The ability to exchange products or services instead of paying with money.” Regarding the impeding factors, the survey gave those respondents who were aware of and users of collaborative platforms the following options for their answers: (1) lack of a responsible person when problems arise (LRESPON) or
“Not knowing who is responsible in case a problem arises”; (2) lack of fulfillment of service expectations (LFULLSERV) or “Being disappointed because the services and goods do not meet expectations”; (3) lack of information (LINFORM) or “Not having enough information on the service provided”; (4) lack of trust in the agents (LTRUSTAG) or “Not trusting the provider or seller”; and (5) lack of trust in the Internet (LTRUSTINT) or “Not trusting the Internet transactions in general.” All of these variables were dichotomous, where 1 = the respondent answered positively about the driving or impeding factors, and 0 = the respondent answered otherwise.
Lastly, the survey gathered sociodemographic data in order to be able to charac- terize the users and the providers of collaborative platforms. Specifically, data were gathered on age, gender, years of education, number of household members, type of locality (village or rural area, small, midsized, or large town/city), and occupa- tional status: self-employed or business person, employee (director, qualified pro- fessional, manual worker, and nonmanual worker), unemployed or nonemployed (stay-at-home parent/carer, student, retiree, or unemployed person).
Table 3 shows the descriptive statistics of the variables relating to the use and provi- sion of collaborative platforms in Europe. Regarding awareness and use of collaborative platforms, the survey found that more than half of European citizens were unaware of these new forms of exchange (53.2%), while a further third was aware of them but had never used them (33.9%). Thus, 12.9% of the European population aged 15 years and above stated that they were users of collaborative platforms, with the following distribu- tion: 3.2% initial use (one transacted exchange), 6.5% occasional use (once every few months), and 3.2% regular use (at least every month). In relation to the provision of goods and services via collaborative platforms, of the users of such platforms (12.9%), almost three quarters had never provided any (72.1%). The remaining 27.9% of users (3.6% of the European population) had provided goods and services, with the following distribution: 7.3% (0.9% of the total) had made an initial provision (provided goods or services once), 15.7% (2.1% of the total) had made an occasional provision (once every few months), and 5.0% (0.6% of the total) had made a regular provision (every month).
For those who were aware of (33.9%) and users of (12.9%) such platforms
(46.8%), the survey also gathered data about the driving and impeding factors of their use. Among the driving factors, convenience (39.1%) and price (31.4%) were cited the most, whereas service newness (22.4%) and the possibility of carrying out nonmon- etary exchanges (21.8%) came some way behind the two main motivations. Regarding the factors that would limit the use and provision of such platforms, the lack of a responsible person when problems arise in the exchange (36.5%) was the main reason given, followed at some distance by the lack of fulfillment of service expectations
N Mean SD Minimum Maximum Skewness Kurtosis Awareness and use
Unaware (UNAWARE)
13,837 0.532 0.499 0 1 −0.128 −1.984
Aware but not use (AWNOTUSE)
13,837 0.339 0.473 0 1 0.682 −1.535
Initial use (INIUSE)
13,837 0.032 0.177 0 1 5.298 26.068
Occasional use (OCCAUSE)
13,837 0.065 0.247 0 1 3.530 10.465
Regular use (REGUSE)
13,837 0.032 0.177 0 1 5.291 26.998
Total use (TOTUSE)
13,837 0.129 0.336 0 1 2.207 2.872
Provision of goods and services No provision
(NOPROV)
1778 0.721 0.448 0 1 −0.987 −1.028
Initial provision (INIPROV)
1778 0.073 0.259 0 1 3.298 8.890
Occasional provision (OCCAPROV)
1778 0.157 0.364 0 1 1.888 1.567
Regular provision (REGPROV)
1778 0.050 0.217 0 1 4.158 15.303
Total provision (TOTPROV)
1788 0.279 0.449 0 1 0.987 −1.028
Driving factors
Price (PRICE) 6477 0.314 0.464 0 1 0.801 −1.359
Newness (NEWNESS)
6477 0.224 0.417 0 1 1.324 −0.247
Convenience (CONVEN)
6477 0.391 0.488 0 1 0.449 −1.779
Nonmonetary (NONMONET)
6477 0.218 0.413 0 1 1.368 −0.127
Impeding factors Lack responsible person
(LRESPON)
6477 0.365 0.481 0 1 0.560 −1.687
Lack fulfilling expect (LFULLSER)
6477 0.259 0.438 0 1 1.099 −0.792
Lack information (LINFORM)
6477 0.186 0.389 0 1 1.614 0.605
Lack trust in agents (LTRUSTAG)
6477 0.250 0.433 0 1 1.154 −0.668
Lack trust in Internet (LTRUSTINT)
6477 0.272 0.445 0 1 1.027 −0.947
Table 3.
The use and provision of collaborative platforms in Europe.
(25.9%), the lack of trust in the Internet in general (27.2%), and the lack of trust in the agents (buyers and sellers) of the exchange in particular (25.0%). Lastly, the lack of information (18.6%) was the reason that the respondents cited the least.
Regarding sociodemographic characteristics, the mean age was 54 years and the majority of the respondents were women (58.4% women, 41.6% men). Of the individuals in the sample, 43.4% had 20 or more years of formal education. From an occupational perspective, of note was the high presence of retirees (37.3%) and of manual workers (20.3%). Most households comprised two members (44.0%). Finally, regarding the localities of European citizens (rural, small or mid-sized town/city, or large metropolitan town/city), the sample was equally divided (into three-thirds).
Furthermore, in relation to countries, the sample skewed toward the European Union’s most populous countries in central and Eastern Europe (35.7% of the sample).
The basic aim of my study is to find out if these sociodemographic characteriza- tion variables, together with the motivation/barrier variables, can be turned into predictors of use and provision behavior on collaborative platforms. To that end, we performed an odds ratio (OR) analysis. Formally, it is usually defined as the ratio of the odds of a condition occurring in a population group to the odds of it occurring in another group. It is a measure of the statistical association between dichotomous variables, which has been widely used in social research for three main reasons:
firstly, because the OR determines a predictor and a confidence interval (95% CI) between binary dichotomous variables, which enables probability relationships to be established; secondly, because it is useful for examining the predictive effect of one variable on another, while the other variables remain constant in a logistic regression model; and thirdly, because OR offers a quick and efficient interpretation in case studies and controls.
The interpretation of an OR analysis is as follows. If the value of the OR is less than 1 and the confidence interval (95% CI) is situated below the unit, the predic- tive relationship between the two variables analyzed is an inverse relationship. If the value of the OR is greater than 1 and the confidence interval (95% CI) is situated above the unit, the predictive relationship between the two variables analyzed is a direct relationship. Whenever the confidence interval (95% CI) includes the unit, the predictive relationship between two variables cannot be determined [46, 47].
If I begin by taking the use of collaborative platforms (n = 1792), the first thing to highlight is that its driving forces are clearly linked to motivations of an economic and practical nature (Table 4). Convenience and price are the two main drivers of col- laborative platform use in Europe. In contrast, the driving factor relating to nonmon- etary exchange, which could be identified as being ideological in an antiestablishment or anticapitalism sense, clearly disincentives the use of collaborative platforms.
Among the impeding forces, it should be noted that the lack of fulfillment of expecta- tions in relation to the service offered via the collaborative platform disincentives the use thereof. In contrast, the lack of trust in the Internet would not act as an impedi- ment to total use.
Among the sociodemographic predictors of the use of collaborative platforms in Europe, the analysis performed provides us with a set of results worth highlighting.
Firstly, men are more inclined than women to use such platforms. Secondly, the younger age ranges (54 years and below) are more likely to make a total use than the older age ranges. And thirdly, households with more members have a greater probability of hav- ing a user of collaborative platforms among them than households with fewer members.
Regarding human capital and occupational status, the joint use of collaborative economy platforms in Europe is also linked to the fact of being a student or having many years of education and to professional contexts of entrepreneurship, manage- rial responsibility, or being highly qualified. In fact, students or people with 20 or more years of formal education are much more likely to use collaborative platforms
Users (n = 1792) Providers (n = 496)
OR (95% CI) OR (95% CI)
Motivations/barriers (driving and impeding factors)
Price 1.687 (1.505–1.890) 1.063 (0.860–1.312)
Newness 1.094 (0.962–1.245) 1.077 (0.846–1.372)
Convenience 2.334 (2.089–2.608) 0.953 (0.775–1.173)
Nonmonetary exchange 0.668 (0.580–0.769) 1.384 (1.062–1.803)
Lack of a responsible person 1.089 (0.973–1.218) 0.747 (0.601–0.929) Lack of fulfillment service
expectation
1.234 (1.093–1.394) 1.234 (0.986–1.544)
Lack of information 1.055 (0.918–1.212) 0.990 (0.760–1.289)
Lack of trust in the agents 1.217 (1.076–1.377) 1.043 (0.828–1.314) Lack of trust in the Internet 0.878 (0.775–0.994) 0.973 (0.767–1.236) Sociodemographic predictors
Age
15–24 years 1.262 (1.039–1.532) 0.871 (0.578–1.311)
25–34 years 2.386 (2.077–2.740) 1.436 (1.106–1.866)
35–44 years 2.097 (1.858–2.367) 0.989 (0.775–1.262)
45–54 years 1.420 (1.260–1.601) 0.878 (0.684–1.595)
55–64 years 0.755 (0.680–0.883) 1.070 (0.815–1.406)
65 years and above 0.246 (0.212–0.286) 0.727 (0.514–1.028)
Gender (1 = male, 0 = female) 1.456 (1.318–1.608) 1.409 (1.144–1.736) Human capital (years of education)
Still studying 1.536 (1.240–1.903) 0.887 (0.570–1.381)
Up to 15 years 0.170 (0.128–0.226) 1.224 (0.669–2.237)
16–19 years 0.616 (0.553–0.687) 0.839 (0.664–1.059)
20 or more years 2.313 (2.088–2.563) 1.170 (0.943–1.453)
Occupational status
Self-employed/entrepreneurs 1.828 (1.573–2.125) 1.843 (1.391–2.443)
Employees—directors 3.012 (2.575–3.522) 1.006 (0.746–1.356)
Employees—qualified professionals
2.181 (1.832–2.596) 1.147 (0.820–1.605)
Employees—nonmanagement workers
1.572 (1.403–1.762) 0.688 (0.539–0.878)
Employees—manual workers 0.781 (0.626–0.974) 1.673 (1.087–2.574)
Nonemployed—parents/
carers
0.598 (0.475–0.754) 0.822 (0.491–1.376)
Nonemployed—students 1.373 (1.092–1.726) 0.787 (0.482–1.284)
Non-employed—retirees 0.271 (0.237–0.310) 0.718 (0.527–0.977)
Unemployed—job seekers 0.886 (0.680–1.153) 1.330 (0.787–2.247)
Household members
One 0.598 (0.524–0.681) 1.200 (0.915–1.574)
Two 1.137 (1.029–1.257) 0.915 (0.742–1.127)
than people with fewer years of education. As far as occupational status is concerned, the self-employed and business people, employees who are directors, employees who are qualified professionals, and employees who are nonmanual workers are the most likely to use collaborative platforms. In contrast, employees who are manual workers, stay-at-home parents/carers, the unemployed and, in particular, retirees are much less inclined toward collaborative consumption via platforms.
Finally, the predictors by geographical area also provide relevant information, firstly, because the impetus behind collaborative consumption comes from large towns/cities and metropolitan areas, whereas living in villages and rural areas would disincentive collaborative consumption via platforms. By country, we also observe a greater likelihood to use collaborative platforms in continental Europe—
Belgium, France, Luxembourg, Netherlands, Austria, and Germany—whereas in Mediterranean Europe—Greece, Spain, Italy, Portugal, Cyprus, Malta, and Croatia—the situation is the inverse.
The analysis of predictive factors for the provision of goods and services via collaborative platforms (n = 496) in Europe (Table 4) reveals a picture that clearly differs from the use of such platforms. Of the motivational predictors of collabora- tive provision, the first element to highlight is that such provision has a clearly ideological component, in an antiestablishment or anticapitalism sense, because the possibility of doing nonmonetary exchanges becomes a driving factor. Moreover, nonmonetary exchange was the only provision-driving predictor to be identi- fied, because the other economic and convenience factors were not significant.
Regarding the impeding forces, the lack of a responsible person would not disincen- tive the collaborative provision of goods and services.
From the perspective of the sociodemographic predictors, the collaborative provision of goods and services in Europe would be motivated by a much nar- rower set of factors than the one identified for collaborative uses. Men, the young
Users (n = 1792) Providers (n = 496)
OR (95% CI) OR (95% CI)
Three 1.212 (1.067–1.377) 1.067 (0.821–1.386)
Four or more 1.203 (1.053–1.374) 0.906 (0.685–1.198)
Locality
Village or rural area 0.736 (0.658–0.823) 1.042 (0.824–1.318)
Small or mid-sized town/city 0.940 (0.848–1.043) 0.980 (0.789–1.217) Large town/city or
metropolitan area
1.419 (1.280–1.574) 0.986 (0.795–1.222)
Country groupings
Continental Europe1 1.249 (1.113–1.403) 1.207 (0.954–1.526)
Mediterranean Europe2 0.735 (0.651–0.831) 1.000 (0.773–1.294)
Northern Europe3 1.058 (0.932–1.202) 0.748 (0.566–0.987)
Central and Eastern Europe4 1.029 (0.928–1.141) 1.028 (0.829–1.276) Notes: OR: odds ratio and 95% CI: confidence intervals at 95%. ORs and 95% CI in bold are significant.
1Continental Europe: Belgium, France, Luxembourg, the Netherlands, Austria, and Germany.
2Mediterranean Europe: Greece, Spain, Italy, Portugal, Cyprus, Malta, and Croatia.
3Northern Europe: Denmark, Finland, Sweden, the United Kingdom, and Ireland.
4Central and Eastern Europe: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia.
Table 4.
Predictors of P2P platform use and provision in Europe.
population aged between 25 and 34 years, the self-employed or entrepreneurs, or manual workers would be the most likely to make collaborative provisions of goods and services. In contrast, nonmanual workers, retirees, or citizens of countries in northern Europe—Denmark, Finland, Sweden, the United Kingdom, and Ireland—
would be the least likely to make collaborative provisions.